Feature(LLMLingua-2): update LLMLingua-2 link

README.md

@@ -4,30 +4,30 @@ license: cc-by-nc-sa-4.0
 
 # LLMLingua-2-Bert-base-Multilingual-Cased-MeetingBank
 
-This model was introduced in the paper [**LLMLingua-2: Data Distillation for Efficient and Faithful Task-Agnostic Prompt Compression** (Pan et al, 2024)](). It is a [XLM-RoBERTa (large-sized model)](https://huggingface.co/FacebookAI/xlm-roberta-large) finetuned to perform token classification for task agnostic prompt compression. The probability $p_{preserve}$ of each token $x_i$ is used as the metric for compression. This model is trained on [an extractive text compression dataset]() constructed with the methodology proposed in the [LLMLingua-2](), using training examples from [MeetingBank (Hu et al, 2023)](https://meetingbank.github.io/) as the seed data.
+This model was introduced in the paper [**LLMLingua-2: Data Distillation for Efficient and Faithful Task-Agnostic Prompt Compression** (Pan et al, 2024)](https://arxiv.org/abs/2403.12968). It is a [XLM-RoBERTa (large-sized model)](https://huggingface.co/FacebookAI/xlm-roberta-large) finetuned to perform token classification for task agnostic prompt compression. The probability $p_{preserve}$ of each token $x_i$ is used as the metric for compression. This model is trained on [an extractive text compression dataset(will public)]() constructed with the methodology proposed in the [**LLMLingua-2**](https://arxiv.org/abs/2403.12968), using training examples from [MeetingBank (Hu et al, 2023)](https://meetingbank.github.io/) as the seed data.
 
-For more details, please check the home page of [LLMLingua-2]() and [LLMLingua Series](https://llmlingua.com/).
+For more details, please check the home page of [LLMLingua-2](https://llmlingua.com/llmlingua2.html) and [LLMLingua Series](https://llmlingua.com/).
 
 ## Usage
 ```python
 from llmlingua import PromptCompressor
 
 compressor = PromptCompressor(
-        model_name="microsoft/llmlingua-2-xlm-roberta-large-meetingbank",
-        use_llmlingua2=True
-    )
+    model_name="microsoft/llmlingua-2-xlm-roberta-large-meetingbank",
+    use_llmlingua2=True
+)
 
 original_prompt = """John: So, um, I've been thinking about the project, you know, and I believe we need to, uh, make some changes. I mean, we want the project to succeed, right? So, like, I think we should consider maybe revising the timeline.
 Sarah: I totally agree, John. I mean, we have to be realistic, you know. The timeline is, like, too tight. You know what I mean? We should definitely extend it.
 """
 results = compressor.compress_prompt_llmlingua2(
-        original_prompt,
-        rate=0.6,
-        force_tokens=['\n', '.', '!', '?', ','],
-        chunk_end_tokens=['.', '\n'],
-        return_word_label=True,
-        drop_consecutive=True
-        )
+    original_prompt,
+    rate=0.6,
+    force_tokens=['\n', '.', '!', '?', ','],
+    chunk_end_tokens=['.', '\n'],
+    return_word_label=True,
+    drop_consecutive=True
+)
 
 print(results.keys())
 print(f"Compressed prompt: {results['compressed_prompt']}")
@@ -50,5 +50,12 @@ for word, label in annotated_results[:10]:
 
 ## Citation
 ```
-{}
+@article{wu2024llmlingua2,
+    title = "{LLML}ingua-2: Context-Aware Data Distillation for Efficient and Faithful Task-Agnostic Prompt Compression",
+    author = "Zhuoshi Pan and Qianhui Wu and Huiqiang Jiang and Menglin Xia and Xufang Luo and Jue Zhang and Qingwei Lin and Victor Ruhle and Yuqing Yang and Chin-Yew Lin and H. Vicky Zhao and Lili Qiu and Dongmei Zhang",
+    url = "https://arxiv.org/abs/2403.12968",
+    journal = "ArXiv preprint",
+    volume = "abs/2403.12968",
+    year = "2024",
+}
 ```